This paper introduces HPNet, a novel deep-learning approach for segmenting a 3D shape
represented as a point cloud into primitive patches. The key to deep primitive segmentation …

We present MultiBodySync, a novel, end-to-end trainable multi-body motion segmentation
and rigid registration framework for multiple input 3D point clouds. The two non-trivial …

We present a robust estimator for fitting multiple parametric models of the same form to noisy
measurements. Applications include finding multiple vanishing points in man-made scenes …

Geometric model fitting is a challenging but fundamental computer vision problem. Recently,
quantum optimization has been shown to enhance robust fitting for the case of a single …

Recently, graph-based methods have been widely applied to model fitting. However, in
these methods, association information is invariably lost when data points and model …

Estimating reliable geometric model parameters from the data with severe outliers is a
fundamental and important task in computer vision. This paper attempts to sample high …

Humans perceive and construct the world as an arrangement of simple parametric models.
In particular, we can often describe man-made environments using volumetric primitives …

Humans perceive and construct the surrounding world as an arrangement of simple
parametric models. In particular, man-made environments commonly consist of volumetric …

We present a real-time method for robust estimation of multiple instances of geometric
models from noisy data. Geometric models such as vanishing points, planar homographies …

We address the problem of estimating the poses of multiple instances of the source point
cloud within a target point cloud. Existing solutions require sampling a lot of hypotheses to …